Object Recognition Using Genetic Algorithms

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Object Recognition UsingGenetic Algorithms

• CS773C Advanced Machine Intelligence Applications
• Spring 2008 Object Recognition

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2D Case

• Recover the geometric transformation that aligns
  the model(s) with the scene.

(affine transformation)
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Image-Space Approaches

• Identify a set of features from the unknown scene
  which approximately match a set of features from
  a model object.
• Recover the geometric transformation that the
  model object has undergone.
• Examples
• Interpretation tree (Grimson Lozano-Perez,
  1987)
• Alignment (Huttenlocher and Ullman, 1990)
• Geometric hashing (Lamdan et al., 1990)

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Transformation-Space Approaches

• Search the transformation space.
• Find a transformation which aligns a large number
  of model features with the scene.
• Examples
• Hough-transform based methods (Ballard, 1981).
• Pose clustering techniques (Cass, 1988)

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Why Using GAs for Object Recognition?

• Genetic algorithms were designed to efficiently
  search large solution spaces.
• Both the image and transformation spaces are very
  large!
• Image space M3S3 possible alignments
• Transformation space much larger!

G. Bebis. S. Louis, Y. Varol, and A. Yfantis,
"Genetic Object Recognition Using Combinations
of Views", IEEE Transactions on Evolutionary
Computation, vol 6, no. 2, pp. 132-146, April
2002.
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Genetic Algorithms (GAs) Review

• What is a GA?
• An optimization technique for searching very
  large spaces.
• Inspired by the biological mechanisms of natural
  selection and reproduction.
• What are the main characteristics of a GA?
• Global optimization technique.
• Uses objective function information, not
  derivatives.
• Searches probabilistically using a population of
  structures (i.e., candidate solutions using some
  encoding).
• Structures are modified at each iteration using
  selection, crossover, and mutation.

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Structure of GA

• 10010110 10010110
• 01100010 01100010
• 10100100... 10100100
• 10010010 01111001
• 01111101 10011101

Evaluation and Selection
Crossover
Mutation
Current Generation
Next Genaration
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Encoding and Fitness Evaluation

• Encoding scheme
• Transforms solutions in parameter space into
  finite length strings (chromosomes) over some
  finite set of symbols.
• Fitness function
• Evaluates the goodness of a solution.

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Selection Operator

• Probabilistically filters out solutions that
  perform poorly, choosing high performance
  solutions to exploit.
• Chromosomes with high fitness are copied over to
  the next generation.

fitness
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Crossover and Mutation Operators

• Generate new solutions for exploration.
• Crossover
• Allows information exchange between points.
• Mutation
• Its role is to restore lost genetic material.

Mutated bit
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Object Recognition Using GasTwo Approaches

• Genetic search in the image space (GA-IS)
• Genetic search in the transformation space
  (GA-TS)
• Important issues
• How to encode solutions?
• How to modify solutions ?
• How to evaluate solutions?

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GA-IS Encoding

• At least three model-scene point matches are need
  to compute the affine transformation.
• Chromosome contains the binary encoded identities
  of the three pairs of points.
• Model points 19 (5 bits)
• Scene points 19 - 45 (6 bits)
• Chromosome length 3 x 5 3 x 6 33 bits

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GA-IS Decoding

• Some encoded solutions might be invalid
• 5 bits can encode at most 32 values.
• 0, 31 was linearlymapped to 0, 18
• 6 bits can encode at most 64 values.
• 0, 63 was linearly mapped to 0, 44

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GA-IS Fitness Evaluation

• Compute affine transformation.
• Apply the transformation on all the model points.
• Compute the back-projection error (BE) between
  the model and scene.
• (dj min distance between the j-th model
  point and the scene)

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GA-TS Encoding

• Need to estimate the range of values that the
  parameters of affine transformation can assume.

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GA-TS Encoding (contd)

• Each chromosome contains six fields.
• Only the range of each coefficient needs to be
  represented.
• e.g., a11 assumes values in -0.408, 0.408
• Its range is 0.408 - (-0.408) 0.816
• 2 decimal digit accuracy 82 values must be
  encoded.
• 7 bits are needed to encode 82 values.
• Chromosome length 6 x 7 42 bits

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GA-TS Decoding

• Some encoded solutions might be invalid
• 7 bits can encode at most 128 values.
• 0, 127 should be mapped to 0, 81

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GA-TS Fitness Evaluation

• Same as before
• Less expensive to compute in this case

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Experiments

• Three scenes (S1, S2, S3) of increasing
  complexity.
• S2, S3 are shown below (S1 was the same as
  model).
• 10 trials per scene tried to find model1 each
  time

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Experiments (contd)

• We searched for the model below in a number of
  scenes.

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GA Parameters

• Two-point crossover (prcoss 0.95).
• Point mutation (pmut 0.05).
• Cross generational selection strategy.
• Fitness scaling (scaling factor 1.2).

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Results Scene 1

• Correct solutions were found in all 10 trials.

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Results Scene 2

• Correct solutions were found in all 10 trials.

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Results Scene 3

• Correct solution was missed once!

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Efficiency GA-IS
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Efficiency GA-TS
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3D case GA-TS

• Apply Genetic Search in the space of the AFoVs
  parameters.
• We used 2 views per model

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Experiments

• Four scenes (S1, S2, S3,S4) of increasing
  complexity.
• S1 was the same as model
• 10 trials per scene

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Experiments (contd)

• We searched for the model below in a number of
  scenes.

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GA Parameters

• Two-point crossover (prcoss 0.95).
• Point mutation (pmut 0.05).
• Cross generational selection strategy.
• Fitness scaling (scaling factor 1.2).
• Population size 200
• Number of generations 150

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Results - Scene 1

• Correct solutions were found in all 10 trials.

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Results Scene 2

• Correct solutions were found in all 10 trials.

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Results Scene 3

• Correct solutions were found in all 10 trials.

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Results Scene 4

• Correct solutions were found in all 10 trials.

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Efficiency GA-TS
2 x 1015
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More Challenging Scene

• GAs can find near-exact matches.
• Could be used as input to more sophisticated
  recognition
• algorithms.